tbuckley/cabot-search huggingface dataset:
Frequency of practice-changing findings identified by comprehensive genomic profiling in non-myeloid hematologic malignancies.
Authors: Katherine I. Zhou, Chenyu Lin, Michelle Green, Bennett Adam Caughey, Michael Datto, John H Strickler, Matthew McKinney
Journal: Journal of Clinical Oncology (2022)

3060 Background: Comprehensive genomic profiling (CGP) is increasingly used to guide management of myeloid and advanced solid malignancies, but its role in non-myeloid hematologic malignancies is less clear. Studies have found a high rate of potentially actionable variants by CGP in this population, but these do not always translate into clinical practice changes. We aimed to determine the rate at which variants found on CGP changed clinical practice. Methods: We retrospectively reviewed a cohort of 101 consecutive patients with non-myeloid hematologic malignancies at Duke, comprising a total of 105 samples that were sent for CGP by FoundationOne Heme (104) or HemeComplete (1) in 2014–2021. We identified variants of clinical significance and classified them by evidence level according to the AMP/ASCO/CAP 2017 guidelines (e.g., for therapies, level A: FDA-approved / in guidelines; B: expert consensus; C: clinical trial / FDA-approved in different tumor type; D: preclinical data). We further identified documented changes in clinical practice that occurred in direct response to CGP results. Results: Commonly cited reasons for CGP included guiding therapy selection (27), identifying resistance mutations (19), refining prognosis (14), and clarifying diagnosis (11). Of the 105 samples sent for sequencing, 92 (88%) yielded at least one pathogenic or likely pathogenic variant. CGP resulted after death in 12 patients and within 1 month of death in another 11 patients. Seventy-three out of 101 patients (72%) had at least one variant with therapy sensitizing, diagnostic, or prognostic significance (levels A–C) or associated with therapy resistance (levels A/D). While 61 patients (60%) had a therapy sensitizing variant, only 6 patients (10%) were offered a biomarker-directed therapy. In contrast, the presence of a resistance mutation led to discontinuation of current therapy or influenced future therapy selection in 9 of 13 patients (69%). The absence of a resistance mutation influenced choice of therapy in another 4 patients. Sequencing results also helped clarify a previously uncertain diagnosis in 4 patients and led to medical genetics referrals in 3 patients. Conclusions: Comprehensive genomic profiling of non-myeloid hematologic malignancies identified variants of clinical significance in 72% of patients and led to changes in practice in 22% of patients. CGP was often sent late in the clinical course. [Table: see text]
Evaluating the use of machine learning use in ovarian cancer: A systematic review.
Authors: Sabrina Piedimonte, Gabriela Rosa, Brigitte Gerstl, Mars Sopocado, Ana Coronel, Salvador Llenno, Danielle Vicus
Journal: Journal of Clinical Oncology (2022)

e17570 Background: Ovarian cancer(OC) is the leading cause of death from gynecologic malignancy. Current challenges include lack of diagnostic tools, predictive biomarkers, and identifying appropriate surgical candidates. Machine learning(ML) is an emerging field that can make accurate projections by making inferences on data and may play a crucial role in OC.The objective of the current study was to review the literature on application of ML in OC and report the most commonly used algorithms and their performance in comparison to existing prediction tools and traditional regression models. Methods: This is a systematic review of published literature from January 1985 to March 2021 on the use of ML in OC. An extensive search of electronic library databases was conducted. Four independent reviewers screened the articles initially by title then full text. Quality was assessed using the MINORS criteria. P-values were generated using the Pearson’s Chi-squared(x 2 ) test to compare performance of ML models with traditional statistics. No p-values were reported if only one study was available. Results: Among 4,295 articles screened, 88 studies on ML in OC were included. The mean age of OC patients was 54.7 years(11-90) and the most common stages at diagnosis were:Stage III (39.9%) and IV (34%). Applications of ML were in clinical datasets(33%, n = 29), preoperative diagnostics(30.7%, n = 27), serum biomarkers (21.6%, n = 19), genomics (12.5%, n = 11), and prediction of cytoreductive outcomes (2.3%, n = 2). The most commonly applied algorithms were Support Vector Machine [SVM](28%, n = 33)and Neural Networks[NN] (25.28%). Over the past decades, the number of publications on ML in OC increased three-fold from 20(1994-2010) to 67 (2011–2021). Only 9 (10%) studies compared ML techniques with existing prediction tools, or traditional regression models. Among 29 clinical dataset studies, 4 compared ML with traditional logistic regression(LR). Two studies reported better performance with ML compared to LR but not significant(accuracy: 0.88 vs 0.84, p = 0.15), one study performed comparably(accuracy: 0.1 vs 0.1) while one study performed worse(accuracy: 0.1 vs 0.97). Only one preoperative diagnostic study compared ML techniques with LR. SVM classifiers outperformed LR in classifying ovarian masses as benign or malignant(sensitivity: 0.88 vs. 0.70). One serum biomarker study compared LR with ML algorithms; LR performed better using two biomarkers for predicting OC(accuracy: 0.97 vs. 0.94). Among five studies reporting overall survival outcomes, only one study compared survival ML techniques using NN with LR and showed that NN classifiers outperformed LR in predicting overall survival(AUC: 0.72 vs. 0.62). Conclusions: This is the first systematic review exploring the literature on ML algorithms in OC. Most ML models outperformed traditional models. However, larger datasets would be required to validate findings.
TALENTop: A multicenter, randomized study evaluating the efficacy and safety of hepatic resection for selected hepatocellular carcinoma with macrovascular invasion after initial atezolizumab plus bevacizumab treatment.
Authors: Hui-Chuan Sun, Feng Shen, Lianxin Liu, Zhi-Yong Huang, Tianqiang Song, Ming Kuang, Bang-De Xiang, Xueli Bai, Xiaodong Zhu, Jian Zhou, Jia Fan
Journal: Journal of Clinical Oncology (2022)

TPS4175 Background: Hepatocellular carcinoma (HCC) patients with macrovascular invasion are consistently considered to be at an advanced stage of disease. The combination therapy of atezolizumab (atezo) plus bevacizumab (bev) has been the new standard-of-care for those patients. In patients responding to systemic therapy, hepatic resection may provide additional benefit. Here we propose a phase 3 study to investigate whether hepatic resection following atezo/bev can bring more benefits for HCC patients with macrovascular invasion when compared with atezo/bev alone. Methods: This is a multicenter, open-label, two-arm, randomized study designed to evaluate the efficacy and safety of surgical resection plus peri-operative atezo/bev compared with regular systemic atezo/bev (Q3W, every three weeks) in HCC patients with macrovascular invasion and without extrahepatic metastasis. Initially eligible patients have enrolled into induction phase, during which they receive 3 cycles of atezo/bev and 1 cycle of atezo alone as primary systemic therapy. Patients who are assessed as partial response or stable disease (RECIST v1.1 criteria) and considered suitable for R0 hepatic resection are randomized in a 1:1 ratio to either Arm A, hepatic resection with post-operative atezo/bev for 1 year (or until loss of clinical benefit or intolerable toxic effects), or Arm B, continuing atezo/bev for 1 year (or until loss of clinical benefit or intolerable toxic effects). The primary endpoint of this study is time-to treatment failure (TTF), defined as time from randomization to the first documented treatment failure (i.e., tumor recurrence or metastasis [Arm A], disease progression [Arm B] according to RECIST v1.1, or death from any cause). We hypothesize that hepatic surgery with peri-operative atezo/bev will improve the TTF from 5.8 months to 9.2 months, with the hazard ratio of 0.63. With 2-sided significance level of 0.05, the sample size for randomization will be 198. The study, registered with clinical trial ID of NCT04649489, started enrollment in Apr 2021. As of Jan 2022, 65 patients have been enrolled and 15 patients have been randomized. Research funding: Shanghai Roche Pharmaceuticals Ltd. Clinical trial information: NCT04649489.
Subgroup analyses in patients with relapsed/refractory multiple myeloma (RRMM) receiving real-life current standard of care (SOC) in the LocoMMotion study.
Authors: Hermann Einsele, Philippe Moreau, Valerio De Stefano, Dominik Dytfeld, Emanuele Angelucci, Reuben Benjamin, Hartmut Goldschmidt, Niels W.C.J. van de Donk, Britta Besemer, Christof Scheid, Ravi Vij, Ester in ’t Groen-Damen, Maria Semerjian, Vadim Strulev, Jordan Mark Schecter, Tito Roccia, Tonia Nesheiwat, Robert Wapenaar, Katja Weisel, Maria-Victoria Mateos
Journal: Journal of Clinical Oncology (2022)

8031 Background: Patients (pts) with RRMM who are triple-class exposed (proteasome inhibitor [PI], immunomodulatory drug [IMiD], and anti-CD38 monoclonal antibody [mAb]) have an urgent and currently unmet clinical need. LocoMMotion (NCT04035226) is the first prospective multinational study of real-life SOC in triple-class exposed pts with RRMM. Here we present efficacy in subgroups of pts treated with SOC therapies in the LocoMMotion study. Methods: LocoMMotion is a noninterventional study across 76 sites (63 European; 13 US). Eligible pts had received ≥3 prior lines of therapy (LOT) or were double refractory to a PI and an IMiD; received a PI, an IMiD, and anti-CD38 mAb; and had disease progression during/after their last LOT. Real-life SOC treatments were defined as those used in local clinical practice. Responses and disease progression were assessed by response review committee, per IMWG criteria. Subgroups were defined by baseline (BL) characteristics: age, Eastern Cooperative Oncology Group performance status (ECOG PS), renal function, ISS stage, presence of extramedullary plasmacytoma, LDH level, % of bone marrow plasma cells, number of prior LOT, triple-class or penta-drug exposure, and refractoriness. Results: As of May 21, 2021 (median follow-up 11.0 mo), 248 pts were enrolled and treated with median 4.0 (range, 1–20) cycles of SOC therapy. Evaluation of efficacy outcomes in subgroups demonstrated that refractoriness to 3 classes of antimyeloma therapy, presence of extramedullary plasmacytomas, high LDH, and ECOG PS ≥1 were associated with generally worse outcomes, compared with pts who did not have these characteristics (Table). Overall response rate (ORR) ranged from 20.0–43.1% across all subgroups. Age and number of prior LOT did not have an impact on efficacy outcomes. Conclusions: Subgroup analyses of this first prospective study of real-life SOC tx in triple-class exposed pts with RRMM indicate that specific pt and disease characteristics were associated with poor outcomes. Triple-class refractory and non-triple-class refractory pts had poor outcomes, although the latter had longer median progression-free survival (PFS). These findings should be considered when planning bridging strategy for CAR-T therapy. Clinical trial information: NCT04035226. [Table: see text]
Rapid access to biomarker data in a community setting: Integration of next-generation sequencing into routine pathologic workflow.
Authors: Kirstin Perdrizet, Parneet Kaur Cheema, Andrea Beharry, Joanne Diep, Marco Iafolla, William Raskin, Shaan Dudani, Mary Anne Brett, Blerta Starova, Brian Olsen, Brandon Sheffield
Journal: Journal of Clinical Oncology (2022)

3143 Background: Biomarker data in the form of next generation sequencing (NGS) are critical to the delivery of precision cancer care. Onsite testing is often limited to large academic centers, requiring smaller community centers to rely on samples send outs. Turnaround time for biomarkers can be lengthy and can adversely affect the delivery of optimal therapy in many tumor types. This study aims to evaluate the feasibility of rapidly delivered comprehensive NGS in a community center using a novel workflow in the laboratory by integrating NGS into the routine immunohistochemistry (IHC) service. Methods: An automated NGS workflow utilizing the Genexus integrated sequencer with the Oncomine precision assay GX (OPA, Thermofisher Scientific), was validated for clinical use and integrated into the routine diagnostic IHC service. During the study period (Oct 2020 – Oct 2021), NGS biomarker data was generated and reported alongside IHC biomarkers where applicable. A retrospective chart review was performed to assess the early experience and performance characteristics of this novel approach to biomarker testing. Results: A total of 578 solid tumor samples underwent genomic profiling. Median turnaround time for biomarker results was 3 business days (IQR 2-5). The majority (n = 481, 83%) of cases were resulted in fewer than 5 business days. Tumor types included lung cancer (n = 310, 54%), melanoma (n = 97, 17%), and colorectal cancer (n = 68, 12%). Specimen types included surgical resections (n = 104, 18%), core biopsies (n = 411, 71%), and cytology specimens (n = 63, 11%). NGS testing detected key driver alterations at expected prevalence rates in respective tumor types; lung EGFR (16%), ALK (3%), RET (1%), melanoma BRAF (43%), colorectal RAS/RAFwild-type (33%), among others. Conclusions: This is the first study demonstrating the clinical feasibility and turnaround time statistics of automated comprehensive NGS performed and interpreted in parallel with diagnostic histopathology and immunohistochemistry in a community setting. This novel approach of integrating biomarkers, IHC, and morphology offers rapid turnaround by removing the need for outsourcing biomarker data. This model could be adopted by other community centers to improve rapid access to biomarker data and therapeutic decision making.
The impact of African ancestry on colorectal cancer disparity.
Authors: Yoo Jin Joung, Michelle Jeung-Eun Lee, Sanjay R. Jain
Journal: Journal of Clinical Oncology (2022)

3621 Background: Colorectal cancer is the second leading cause of cancer deaths in the world to affect both men and women. Racial disparities have been known to affect the disease diagnosis and progression, and proliferative genomic studies have been undertaken to elucidate their relationship. In this study, we investigate the tumor mutation profile in colorectal adenocarcinoma (COAD) based on genetic ancestry using The Cancer Genetic Ancestry Atlas (TCGAA). Methods: 59 AA (African American) individuals in TCGAA COAD dataset were identified. To account for and eliminate the biases introduced by self-identification of races, we utilized TCGAA genomic analysis to accurately estimate the ancestral genomic composition of each individual. For each individual, percentages of European (EA), West African (WA), East Asian (EAA), and Native American (NA) ancestry were identified based on Local Ancestry in adMixed Populations (LAMP). Individuals were screened for dominant WA ancestry (≥50% based on LAMP) and assigned to three groups, ≥90%, 80-89%, and 50-79%. Differences in gene mutation frequency and overall survival were compared among the three subgroups of individuals with WA ancestry. Results: Based on genomic ancestry analysis, 58 individuals with dominant WA ancestry (Range 53%-100%) were identified. We classified them into three groups based on percentage of WA ancestry: ≥90% (n = 17), 80-89% (n = 26), and 50-79% (n = 15). APC was the most frequently mutated gene in all groups except ≥90% WA ancestry, which had the most mutation frequency in TP53. ≥90% WA ancestry showed the highest rate of mutation of 81.3% in TP53 compared to 70.8% (80-90% WA ancestry) and 40.0% (50-79% WA ancestry). Interestingly, ≥90% WA ancestry had the highest average rate of mutation (33%) for 7 tumor suppressor genes ( AMER1, APC, ARID1, FBXW7, TCF7L2, TGFBR2, and TP53), compared to 29% and 22% in 80-89% WA ancestry and 50-79% WA ancestry, respectively. In addition, ≥90% WA ancestry had a lower rate (19%) of mutation in 6 oncogenes ( BRAF, NRAS, KRAS, PIK3CA, SMAD4, and SOX9) compared to 24% in both 80-89% and 50-79% WA ancestry. Although not statistically significant, a higher percentage of WA ancestry in an individual was correlated with a downward trend in the overall survival rate (median survival 56.3 months in ≥90% WA ancestry vs 61.8 months in 80-89% WA ancestry). Conclusions: In this study, we analyzed different gene mutations correlated with African ancestry and their potential relationship to the etiology, progression, and prognosis of COAD. The mutation profile of these genes will allow us to investigate altered pathways associated with African ancestry and draw insight into colon cancer pathogenesis in various ancestry groups. Further studies are warranted to elucidate the role of genetic ancestry in tumorigenesis and disease progression and to identify potential therapeutic targets specific to groups disproportionately affected by cancer.
Real-world genomic testing, treatment, and outcomes in patients with cholangiocarcinoma with FGFR2 fusions/rearrangements.
Authors: Mitesh J. Borad, Therese Conner, LiHong He, Reza Kazerooni, Tehseen Salimi, Robert S. Epstein
Journal: Journal of Clinical Oncology (2022)

e16168 Background: Several fibroblast growth factor receptor (FGFR) inhibitors have been recently approved or are in development for the treatment of cholangiocarcinoma (CCA) with FGFR2 fusions/rearrangements (f/r), such as the ATP-competitive inhibitors pemigatinib and infigratinib (FDA-approved in 2020/2021) and the covalent inhibitor futibatinib, which has shown efficacy in a pivotal phase 2 trial. To inform future evaluations of the impact of these agents in clinical practice, and understand other existing gaps in care, we investigated the characteristics and clinical management of patients with CCA and FGFR2 f/r prior to 2021. Methods: This real-world, retrospective cohort study used de-identified, patient-level electronic health records data from Cancer Treatment Centers of America (CTCA) to identify adults with a diagnosis of advanced intrahepatic CCA (iCCA) who received genomic testing between 2013-2021. Clinical/demographic characteristics, time to genomic testing, treatment patterns, and outcomes of patients with and without FGFR2 f/r were described. Results: 23 patients with FGFR2 f/r and 88 with FGFR wild-type (wt) iCCA were identified. Compared to FGFR wt, FGFR2 f/r had a higher proportion of female (65.2 vs 45.5%) and Black (39.1 vs 18.2%) patients (Table). Mean age at diagnosis was ̃60 y and &gt;60% were diagnosed with stage 4 disease in both cohorts; 91.3% of f/r and 78.4% of wt patients had an ECOG PS 0/1. Median time from diagnosis to genomic testing was ̃5 mo; upon entry to CTCA, median time to testing was &lt;2 mo. 17/23 patients in the f/r cohort had FGFR2 fusions (4 BICC1, 13 other). Three FGFR2 f/r patients received an FGFR inhibitor (pemigatinib) as next treatment after testing in 2020. A trend towards longer median overall survival in the f/r vs wt group (24.5 vs 17.8 mo), and a correlation of survival with ECOG PS were observed. Conclusions: This analysis provides insight into the real-world characteristics, clinical management and outcomes among patients with iCCA and FGFR2 f/r, predominantly prior to the introduction of FGFR inhibitors, providing a baseline for further study as the treatment landscape evolves. Of note, the time lag between diagnosis and genomic testing before entry to CTCA underlines the need for earlier testing to guide optimal therapy in clinical practice. [Table: see text]
Efficacy and safety of rucaparib maintenance treatment in patients from ARIEL3 with platinum-sensitive, recurrent ovarian carcinoma not associated with homologous recombination deficiency.
Authors: Robert L. Coleman, Amit M. Oza, Domenica Lorusso, Carol Aghajanian, Ana Oaknin, Andrew Dean, Nicoletta Colombo, Johanne I Weberpals, Andrew R. Clamp, Giovanni Scambia, Alexandra Leary, Robert W. Holloway, Margarita Amenedo Gancedo, Peter C.C. Fong, Jeffrey C. Goh, David M. O'Malley, Sandra M. Goble, Lara Maloney, Jonathan A. Ledermann
Journal: Journal of Clinical Oncology (2022)

5544 Background: In ARIEL3 (NCT01968213), rucaparib maintenance treatment led to significant improvement vs placebo for the primary endpoint of investigator-assessed progression-free survival (PFS) in patients (pts) with platinum-sensitive, recurrent ovarian carcinoma responsive to the last line of platinum therapy (Coleman et al. Lancet. 2017;390:1949–61). The largest benefit was observed in pts with carcinomas with a BRCA mutation or high loss of heterozygosity (LOH), a marker of homologous recombination deficiency (HRD). However, rucaparib also improved PFS in pts with carcinomas negative by HRD test (ie, BRCA wild-type with low LOH), a subset of pts for which there is no identified molecular mechanism conferring PARP inhibitor sensitivity. Among these pts (rucaparib, n = 107; placebo, n = 54), median PFS was 6.7 vs 5.4 months, respectively (HR, 0.58 [95% CI 0.40–0.85]; P= 0.0049), and 31.8% vs 4.3% were progression-free at 1 yr. In this post hoc exploratory analysis, we further evaluated the efficacy of rucaparib maintenance vs placebo in this subset of pts. Methods: Pts were randomized 2:1 to oral rucaparib (600 mg BID) or placebo. For this analysis, investigator-assessed PFS and safety were evaluated in pts with HRD-negative carcinoma, defined as BRCA wild-type with genomic LOH &lt; 16% using Foundation Medicine’s T5 NGS assay. Results: Visit cutoff dates for efficacy and safety were Apr 15, 2017, and Dec 31, 2019. Across subgroups based on demographic or disease characteristics, the trend of rucaparib benefit vs placebo was consistently observed in pts with HRD-negative carcinoma (Table). The safety profile of rucaparib in the HRD-negative population was consistent with that of the overall safety population reported previously. Conclusions: Rucaparib maintenance reduced risk of progression in pts with ovarian carcinomas, including those not associated with HRD, regardless of clinical prognostic factors. [Table: see text]